This paper presents a new multiscale transformation for statistical analysis of one-dimensional data such as time series under the concept of the scale-space approach. The proposed method uses regular obser-vations (eye scanning) with a range of different intervals. The new approach, termed 'elastic-band trans -form,' can be considered as a collection of observations over various intervals (length of elastic-band) of viewing. It is motivated by how people look at an object, such as a sequence of data repeatedly to overview a global structure of the object and find some specific features of it. Some measures based on the transformed elastic-bands are discussed for describing characteristics of data, and multiscale visual-izations induced by the measures are developed for a better understanding of data. Several numerical experiments are performed to demonstrate the usefulness of the proposed transform for visualization and detection.(c) 2023 Elsevier B.V. All rights reserved.
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